What is the true competition in the AI agent economy?

Author: Han Qin, Jarsy CEO

A friend asked, what is the true competition in the AI Agent Economy?

Actually, many friends have shared their thoughts and insights on whether AI Agents really need crypto.

But what we should really discuss is not whether AI Agents need Visa or Crypto, but whether AI Agents require a “traditional credit system” or an “algorithmic trust system.”

This question touches on the fundamental divergence point of future financial structures: should human society rely on human-backed credit or on mathematically guaranteed trust?

Since Bitcoin provided a proof of mathematical trust, this topic has gained significance.

Let’s first define the two systems. The essence of a credit system is trusting that a subject will not default; trust sources include legal reputation, regulatory identity, intermediaries. The core structure is from individuals to institutions, to rules, to trust. Typical examples include banking systems, Visa & Mastercard, securities markets, lending contracts, etc.

In contrast, the algorithmic trust system’s essence is that no trust in any person is needed; trust sources are mathematical proofs, cryptography, signatures, consensus, tamper-proof ledgers. The core structure is from code to mathematics, to automatic execution, to trust. Typical examples include blockchain, smart contracts, zk proofs, MPC, etc.

The fundamental difference between the two lies in philosophy. The trust source of a credit system is human institutions; failure occurs through human default, correction via courts, with borders defined by nations. The trust source of an algorithmic trust system is mathematical theorems; failure occurs through code vulnerabilities, correction via forks, with borders defined by networks. Essentially, credit systems trust human entities, while algorithmic systems trust code rules.

Why did human society initially rely solely on credit systems? Because historically, the technological conditions for algorithmic trust did not exist. To realize algorithmic trust, one needs public key cryptography, distributed networks, consensus algorithms, verifiable computation—all of which have only emerged in recent decades. Therefore, for thousands of years, the only feasible approach was to find someone everyone trusts—be it elders, kings, or in modern times, banks and central authorities.

Why is the AI era pushing toward algorithmic trust? Because AI changes the structure of transaction subjects. In the past, transaction subjects were humans; now, they are AI Agents plus humans. The problem is, machines cannot understand legal reputation or social relationships; they only understand verifiable rules.

Therefore, an AI-native economy will inevitably lean toward an algorithmic trust system; otherwise, machines cannot participate smoothly.

Of course, the advantages of the credit system will not disappear. Many mistakenly believe that crypto will replace the credit system—that’s impossible. Because the credit system is naturally suited for high-uncertainty worlds, such as venture capital, healthcare, war, startups. These scenarios cannot be predefined by code and require judgment and flexible consensus, which algorithms cannot handle.

Secondly, real-world society needs human error correction. Fraud, mistakes, and gray areas will inevitably occur, and only humans can adjudicate these. Long-term trust relationships also still rely on traditional credit systems, such as family trusts, political alliances, strategic partnerships—these depend on relationship capital, not algorithms.

However, the advantages of the algorithmic trust system are exploding. Today, we cannot see this clearly because the breakout point has not yet arrived. In high-frequency trading environments, machine speeds far surpass human trust speeds, giving crypto a crushing advantage. Additionally, cross-border transactions will naturally favor crypto, as algorithms are indifferent to borders. Of course, the permissionless scenarios we’ve discussed are even more the main battlefield for crypto.

In the future, the real world will not be a binary choice; the ultimate structure will definitely be a layered trust architecture. The top layer is the trust governance layer, responsible for rule-making, dispute resolution, with risk-bearing entities still being national courts. The middle layer is the protocol execution layer, responsible for automatic execution, asset transfer, with clearing handled by blockchain and smart contracts. The bottom layer is the computational verification layer, responsible for cryptographic proofs, data integrity, and consensus algorithms.

The greatest future competition will not be crypto versus banks, but who defines the trust interface standards. Whoever sets the standards controls the ecosystem. History has already shown that TCP/IP defined the internet, SWIFT defined financial communication, Visa defined consumer payments. The next standard could be a programmable trust protocol to define the AI Agent Economy.

If the credit system is like a legal state, then the algorithmic trust is like an automated machine society. Their relationship is not substitution but that laws set the rules, and machines execute those rules.

The credit system answers who is trustworthy; the algorithmic trust system answers how to trust without trusting anyone.